Bibliographic record
Abstract
One summer afternoon in Tbilisi, my friends Elizbari and Malkhazi, both native Tbilisians, and I bought some beer from a local store near Malkhazi's home in the hillside residential Tbilisi neighborhood of K'rts’anisi. For various reasons I can no longer recall, it would not do for us to drink in his home, so we randomly chose a deserted spot nearby: a patch of gravel next to a decrepit building with a large fallen tree, which afforded us a place to sit. Malkhazi surveyed our abject drinking spot, raised his beer in a heroic pose, and proclaimed: “Ortach'alis baghshi mnakhe, vina var!” (In the gardens of Ortachala see me, who I am!).1 We laughed at the absurd poetic reference. It was a famous line from a Persian-style Georgian poem by the noble romantic poet Grigol Orbeliani. It was a mukhambazi, a genre of poetry emblematic of “Old Tbilisi” city poetry associated with a nostalgic Georgian mythology of the nineteenth-century colonial city, centering on the island gardens of Ortachala, the site of drunken feasting of typical Tbilisian street peddlers called kintos (Georgian k'int’o). The stanza goes as such: In the gardens of Ortachala see me, who I am, In a happy-go-lucky feast see me, who I am! A toastmaster with a drinking bowl, see me, who I am! Well in a fistfight see me, who I am! Then you will fall in love with me, say, “You are precious!”
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.021 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".